# JITLine: A Simpler, Better, Faster, Finer-grained Just-In-Time Defect Prediction

arXiv: https://arxiv.org/abs/2103.07068 · HTML: https://mlsanity.com/paper/2103.07068/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Mar 2021.

## Built on by (most cited first, 15 of 15)

- [On Formal Feature Attribution and Its Approximation](https://mlsanity.com/paper/2307.03380/) (2023)
- [RovoDev Code Reviewer: A Large-Scale Online Evaluation of LLM-based Code Review Automation at Atlassian](https://mlsanity.com/paper/2601.01129/) (2026)
- [AI for DevSecOps: A Landscape and Future Opportunities](https://mlsanity.com/paper/2404.04839/) (2024)
- [ApacheJIT: A Large Dataset for Just-In-Time Defect Prediction](https://mlsanity.com/paper/2203.00101/) (2022)
- [A study on the impact of pre-trained model on Just-In-Time defect prediction](https://mlsanity.com/paper/2309.02317/) (2023)
- [Data and Context Matter: Towards Generalizing AI-based Software Vulnerability Detection](https://mlsanity.com/paper/2508.16625/) (2025)
- [AgenticSCR: An Autonomous Agentic Secure Code Review for Immature Vulnerabilities Detection](https://mlsanity.com/paper/2601.19138/) (2026)
- [Just-In-Time Software Defect Prediction via Bi-modal Change Representation Learning](https://mlsanity.com/paper/2410.12107/) (2024)
- [Rethinking Code Review in the Age of AI: A Vision for Agentic Code Review](https://mlsanity.com/paper/2605.17548/) (2026)
- [Predicting Line-Level Defects by Capturing Code Contexts with Hierarchical Transformers](https://mlsanity.com/paper/2312.11889/) (2023)
- [Beyond Fidelity: Explaining Vulnerability Localization of Learning-based Detectors](https://mlsanity.com/paper/2401.02686/) (2024)
- [Mining Action Rules for Defect Reduction Planning](https://mlsanity.com/paper/2405.13740/) (2024)
- [On the calibration of Just-in-time Defect Prediction](https://mlsanity.com/paper/2504.12051/) (2025)
- [CodeFlowLM: Incremental Just-In-Time Defect Prediction with Pretrained Language Models and Exploratory Insights into Defect Localization](https://mlsanity.com/paper/2512.00231/) (2025)
- [Typed Temporal Interaction Features for Simulation-Backed Forecasting of Open-Source Game Release Incidents](https://mlsanity.com/paper/2609.31647/) (2026)

## Abstract (arXiv)

A Just-In-Time (JIT) defect prediction model is a classifier to predict if a commit is defect-introducing. Recently, CC2Vec -- a deep learning approach for Just-In-Time defect prediction -- has been proposed. However, CC2Vec requires the whole dataset (i.e., training + testing) for model training, assuming that all unlabelled testing datasets would be available beforehand, which does not follow the key principles of just-in-time defect predictions. Our replication study shows that, after excluding the testing dataset for model training, the F-measure of CC2Vec is decreased by 38.5% for OpenStack and 45.7% for Qt, highlighting the negative impact of excluding the testing dataset for Just-In-Time defect prediction. In addition, CC2Vec cannot perform fine-grained predictions at the line level (i.e., which lines are most risky for a given commit). In this paper, we propose JITLine -- a Just-In-Time defect prediction approach for predicting defect-introducing commits and identifying lines that are associated with that defect-introducing commit (i.e., defective lines). Through a case study of 37,524 commits from OpenStack and Qt, we find that our JITLine approach is at least 26%-38% more accurate (F-measure), 17%-51% more cost-effective (PCI@20%LOC), 70-100 times faster than the state-of-the-art approaches (i.e., CC2Vec and DeepJIT) and the fine-grained predictions at the line level by our approach are 133%-150% more accurate (Top-10 Accuracy) than the baseline NLP approach. Therefore, our JITLine approach may help practitioners to better prioritize defect-introducing commits and better identify defective lines.